CS-GY 6953 Deep Learning Major Project

Drowsy Driver Detection System using OpenCV and CNN . A real-time drowsiness detection system that alerts drivers when signs of drowsiness are detected using computer vision and deep learning. This project leverages OpenCV for video capture and CNN for eye state classification.

SomnoGuard is a real-time drowsiness and fatigue detection system for drivers. Using deep learning algorithms, it monitors the driver's eyes and yawning patterns to detect signs of drowsiness and fatigue. The system provides real-time alerts to drivers, aiming to reduce the risk of accidents caused by driver fatigue.

Real-time Driver Drowsiness Detection system using Deep Learning (CNN). Built on a Client-Server architecture (Python/PyTorch backend + C# WinForms Client) to detect eye fatigue and yawning.

Driver Drowsiness Detection System using Mediapipe and deep learning combines real-time facial landmark tracking, feature extraction, and a trained neural network to monitor drivers for signs of drowsiness. This technology contributes to road safety by helping to prevent accidents caused by driver fatigue.

Developed a Driver Drowsiness Detection System using Raspberry Pi 4 and YOLO-based image processing to monitor eye state in real time. The system detects drowsiness by analyzing prolonged eye closure and triggers alerts to improve driver safety. Combined computer vision, deep learning, and embedded hardware into a cost-effective solution.

Sleep Detector is an AI-powered system that detects driver drowsiness in real time using deep learning and computer vision. It analyzes facial features from a webcam to classify drivers as drowsy or alert, helping prevent fatigue-related accidents. Built with Keras and TensorFlow, the model is trained on a labeled dataset for accurate detection.

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Real-time driver drowsiness detection system using deep learning, facial cues (eye closure, yawning, head posture), and temporal feature fusion.

Real-time Drowsiness Detection System using computer vision and deep learning. The system analyzes live camera feed for signs of drowsiness in drivers' eyes and triggers alarms

A real-time driver drowsiness detection system using CNN-LSTM deep learning architecture, trained on NTHU, YAWDD, and custom datasets with video-based yawning recognition.

Built a Driver Drowsiness Detection System using Machine Learning and IoT technologies to monitor and alert drivers in real-time for signs of drowsiness or distraction during long journeys. Technologies used : Python ,OpenCV,Deep learning, camera, Tensorflow, Haar cascades,Arduino.

A real-time drowsiness detection system built using deep learning to ensure safer driving conditions by alerting drivers when signs of fatigue are detected via eye-state classification.

This project implements a **Drowsy Driver Detection System** using deep learning. The system can classify a driver's state as **Alert, Microsleep, or Yawning** in real-time using a webcam.

Real-time driver drowsiness detection system using computer vision and deep learning. Monitors eye movements through webcam feed and triggers alerts when drowsiness is detected. Built with Streamlit, TensorFlow, and OpenCV for easy web-based access and deployment.

Driver drowsiness detection system using deep learning to enhance road safety. Utilizes computer vision and a trained InceptionV3 model to monitor driver's eye state in real-time, alerting when signs of fatigue are detected.

Driver Drowsiness Detection System is a deep learning–based application using Python and CNN to detect driver fatigue in real time through facial features. It includes a secure user login interface, real-time monitoring, and alert system to enhance road safety and prevent accidents.

Driver Drowsiness Detection System is a computer vision project that monitors eye states (open/closed) in real time using a webcam. A trained deep learning model detects drowsiness, and if eyes remain closed beyond a threshold, an alarm is triggered to alert the driver.

A real-time web-based driver drowsiness detection system that uses a deep learning model to detect fatigue, triggers an audio alarm, and automatically sends an SOS email alert if the driver does not respond.

Real-time drowsiness detection using EAR, MAR, and 3D head pose with Mediapipe and OpenCV. Features automatic user calibration, smooth multi-cue prediction, and an audio alert system. Runs on CPU without deep learning. Ideal for driver safety, monitoring, and real-time fatigue detection.

A real-time, deep learning-based system designed to monitor and detect drowsiness in drivers using computer vision techniques. This project employs facial landmarks, eye state detection, gaze direction tracking, and head pose estimation to ensure safer driving by alerting drivers when drowsy or distracted.

A deep learning–based driver drowsiness detection system using MobileNetV2 to analyze facial cues such as eye closure and yawning. Trained on the YawDD dataset, it classifies driver states in real time to improve road safety and reduce fatigue-related accidents.

The Real-Time Drowsiness Detection system was developed using Python and OpenCV. This technology is designed to enhance safety by preventing accidents caused by drivers who fall asleep while driving. In a Drowsiness Detection project using Python and OpenCV, we use OpenCV to capture images from the webcam and feed them into a Deep Learning model,

This project is a real-time drowsiness detection system that helps prevent accidents by monitoring signs of driver fatigue. Using OpenCV and Deep Learning (CNN or Dlib), it analyzes facial landmarks such as eye closure duration and yawning frequency. If drowsiness is detected, the system triggers an alert (sound or visual warning).

No-Nap Drive is a real-time driver drowsiness detection system using computer vision and deep learning. It extracts Eye Aspect Ratio (EAR) via MediaPipe, analyzes temporal patterns using a 3-class LSTM model (Alert, Drowsy, Critical), and triggers severity-based alerts to help prevent microsleep-related accidents.

Real-time driver drowsiness detection system using computer vision, deep learning, and alert notifications via Twilio API.

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Real-Time Driver Drowsiness Detection System using Deep Learning and MediaPipe.

Real-time driver drowsiness detection system using computer vision and deep learning.

😴 Real-time driver drowsiness detection using OpenCV & deep learning — safety AI system